Variations of lung nodule invasiveness and morphology relate to prognosis and patient outcomes. One approach for diagnosing cancer is histopathological examination of biopsy tissue. The examination may produce a diagnostic profile based on attributes including cell morphology, cytoplasmic changes, cell density, and cell distribution. Visual characterization of tumor morphology is, however, time consuming and expensive. Visual characterization is also subjective and thus suffers from inter-rater and intra-rater variability. Conventional visual characterization of lung nodule morphology by a human pathologist may therefore be less than optimal in clinical situations where timely and accurate classification can affect patient outcomes.
Computerized tomography (CT) is used to image nodules in lungs. Chest CT imagery may be used to detect and diagnose non-small cell lung cancer. However, conventional approaches have been challenged when defining radiographic characteristics that reliably describe the degree of invasion of early non-small cell lung cancers with ground glass opacity (GGO). For example, conventional CT imagery based approaches may find it difficult, if even possible at all, to reliably discriminate nodules caused by benign fungal infections from non-small cell lung cancer nodules.
The degree of invasion of a lung nodule is correlated with prognosis. For example, patients suffering from minimally invasive nodules may have higher disease free survival rate at five years compared to patients with nodules demonstrating frank invasion. Since radiologists may be challenged to reliably distinguish the level of invasiveness of lung nodules in situ using conventional CT approaches in clinically optimal or relevant time frames, invasive procedures that may be performed that ultimately result in a negative diagnosis. These invasive procedures take time, cost money, and put a patient at additional risk.